MOBILE CRM IN INDUSTRIAL FIRMS:
SALESPEOPLE’S INTENTION TO ADOPT MOBILE SOLUTIONS
JYVÄSKYLÄN YLIOPISTO
TIETOJENKÄSITTELYTIETEIDEN LAITOS 2013
Nisula, Rosa; Pirttiniemi, Janne
Mobile CRM in industrial firms: Salespeople’s intention to adopt mobile solu- tions
Jyväskylä: University of Jyväskylä, 2013, 119 p.
Information Systems, Master’s Thesis
Supervisor(s): Frank, Lauri; Karjaluoto, Heikki
Mobile technology and services have gained attention in organisations and aca- demia alike in terms of what are their impact on organisational performance.
There is however little information on what drives salespeople to adopt mobile technology. Our study contributes this discourse by investigating what are the antecedents of behavioural intention when regarding the use of mobile custom- er relationship management in the course of sales managers’ work.
To examine this, a conceptual model was developed based on literature on technology adoption and acceptance research. Specific insight was sought from literature focusing on mobile technology adoption and sales automation tech- nology.
This study utilised exploratory quantitative approach in order to empiri- cally test the model. An online survey was sent to 5 Finnish firms operating in industrial sector. In total, the survey gathered 105 responses globally. The mod- el was tested via structural equation modelling (SEM), and more specifically partial least squares (PLS) was chosen to estimate the parameters of SEM.
Our findings indicated that behavioural intention was sufficiently ex- plained by perceived behavioural control, perceived usefulness and perceived reachability. Social influences explained both key technology related beliefs (i.e., perceived ease of use and perceived usefulness), whereas personal innovative- ness only explained perceived ease of use. Contrary to our hypothesis, attitude did not exert sufficient effect on behavioural intention.
Keywords: technology acceptance, TAM, mobile technology adoption, mobile CRM, PLS
Nisula, Rosa; Pirttiniemi, Janne
Mobile CRM in industrial firms: Salespeople’s intention to adopt mobile solu- tions
Jyväskylä: Jyväskylän yliopisto, 2013, 119 s.
Tietojärjestelmätiede, pro gradu-tutkielma Ohjaaja(t): Frank, Lauri; Karjaluoto, Heikki
Mobiiliteknologian ja -sovellusten vaikutus organisaatioiden suorituskykyyn on herättänyt huomiota sekä organisaatioissa että akateemisessa maailmassa. Ei kuitenkaan vielä tarpeeksi tiedetä siitä, mikä saa myyntihenkilöstön omaksu- maan mobiiliteknologiaa. Tämä tutkimus osallistuu tähän keskusteluun tutki- malla, mitkä tekijät vaikuttavat myyntipäälliköiden aikomuksiin käyttää mobii- lia asiakkuuksienhallintajärjestelmää.
Tämän tutkimiseksi, käsitteellinen malli rakennettiin teknologian hyväk- symiseen ja omaksumiseen liittyvän kirjallisuuden pohjalta. Erityistä näkemys- tä haettiin mobiiliteknologian omaksumista ja myyntiä tukevia teknologioita käsittelevästä kirjallisuudesta.
Tutkimus hyödynsi kokeellista ja määrällistä tutkimusotetta testatakseen luotua mallia. Online-kysely lähetettiin viidelle suomalaiselle teollisuusyrityk- selle. Kaikkiaan kysely keräsi 105 vastausta maailmanlaajuisesti. Mallia testat- tiin käyttäen rakenneyhtälömallinnusta (structural equation modelling, SEM) ja tarkemmin ottaen osittainen pienimmän neliösumman regressioanalyysi (par- tial least squares, PLS) valittiin arvioimaan rakenneyhtälön muuttujia.
Löydöksemme osoittivat, että käyttöaikomusta pystytään merkittävästi se- littämään havaitun käyttäytymiseen liittyvän kontrollin, havaitun hyödyllisyy- den ja havaitun tavoiteltavuuden avulla. Sosiaaliset vaikuttimet selittivät mo- lempia määrääviä teknologiaan liitettäviä uskomuksia (eli havaittua helppo- käyttöisyyttä ja hyödyllisyyttä), kun taas henkilökohtainen innovatiivisuus se- litti ainoastaan havaittua helppokäyttöisyyttä. Asenteella ei ollut vastoin hypo- teesiamme merkittävää vaikutusta käyttöaikomukseen.
Asiasanat: teknologian hyväksyminen, TAM, mobiiliteknologian omaksuminen, mobiili asiakkuuksien hallinta, PLS
We would like to thank our instructors Lauri Frank and Heikki Karjaluoto for giving us insightful ideas and encouraging us in the writing process of this the- sis. We would also like to thank Heikki Karjaluoto and Aarne Töllinen for giv- ing us the possibility to work with DIMAR project.
As this thesis is an outcome from the collaboration of two writers, it is necessary to provide the reader with some means to assess the individual con- tribution of both writers. However, we would like to stress that the areas of re- sponsibility are only indicative, as we ourselves see that this thesis is an out- come of group work, rather than separate individual contributions.
Following table illustrates the areas of responsibilities. Some chapters are illustrated in more detail, as the workload could be clearly attributed to either individual.
Chapter Rosa Nisula Janne Pirttiniemi
1. Introduction x x
2. Technology adoption and ac- ceptance
x 3. Mobile technology adoption x
3.3. Conclusion on mobile technology
adoption research x x
4. Sales automation technology x
5. Methodology x
5.1. Research process x
5.2. Conceptual research model x x
5.3. Hypothesis generation x x
5.4. Quantitative research approach x 5.5. Statistical analysis technique: struc-
tural equation modelling (SEM)
x
6. Results x x
7. Discussion x x
8. Summary x x
FIGURE 1 Variables determining the rate of adoption of innovations (Rogers,
2003, p. 222) ... 16
FIGURE 2 The diffusion process (Rogers, 2003, p. 11) ... 17
FIGURE 3 Adopter categorisation on the basis of innovativeness (Rogers, 2003, p. 281) ... 18
FIGURE 4 Theory of reasoned action (Davis, Bagozzi, & Warshaw, 1989, p. 984) ... 20
FIGURE 5 Theory of planned behaviours (Ajzen, 1991) (adapted from Mathieson, 1991, p. 175) ... 21
FIGURE 6 Technology acceptance model (Davis et al., 1989, p. 985) ... 22
FIGURE 7 Popular extensions to TAM (adapted from Wixom & Todd, 2005) .. 24
FIGURE 8 Technology acceptance model 2 (adapted from Venkatesh & Davis, 2000, p. 188) ... 24
FIGURE 9 Determinants of perceived ease of use (Venkatesh, 2000, p. 346) ... 25
FIGURE 10 Technology acceptance model 3 (TAM3) (Venkatesh & Bala, 2008, p. 280) ... 26
FIGURE 11 Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003, p. 447) ... 28
FIGURE 12 Research directions in mobile service adoption studies (Pedersen & Ling, 2003, p. 2) ... 36
FIGURE 13 Salesperson’s usage of technology (Buehrer, Senecal, & Pullins, 2005, p. 394) ... 55
FIGURE 14 The customer relationship management process (Zablah, Bellenger, & Johnston, 2004, p. 482) ... 56
FIGURE 15 Conceptual research model and hypotheses ... 62
FIGURE 16 Formative and reflective indicators (adapted from Haenlein & Kaplan, 2004, p. 289) ... 74
FIGURE 17 Respondent distribution globally ... 80
FIGURE 18 Respondents' career lengths in years in current organisations ... 80
FIGURE 19 Results of the final model. ... 90
TABLES TABLE 1 Synthesis on technology acceptance and adoption research ... 31
TABLE 2 Constructs related to mobile adoption research in literature ... 51
TABLE 3 Research variables and their conceptual descriptions ... 63
TABLE 4 Survey constructs and indicators ... 69
TABLE 5 Descriptive statistics for intention indicators ... 81
TABLE 6 Descriptive statistics for attitude indicators ... 82
TABLE 8 Descriptive statistics for perceived ease of use indicators ... 83
TABLE 9 Descriptive statistics for personal innovativeness indicators ... 84
TABLE 10 Descriptive statistics for social influence indicators ... 84
TABLE 11 Descriptive statistics for perceived behavioural control indicators .. 85
TABLE 12 Descriptive statistics for perceived reachability indicators ... 86
TABLE 13 Reliability and validity of the model ... 87
TABLE 14 Correlation matrix and square root of the AVE on the diagonal ... 87
TABLE 15 Results of structural model, **p ≤ 0,01 and * p ≤ 0,05 ... 88
TABLE 16 Coefficient of determination and predictive relevance ... 89
TABLE 17 Effect size on behavioural intention ... 89
ABSTRACT ... 2
TIIVISTELMÄ ... 3
PREFACE ... 4
FIGURES ... 5
TABLES ... 5
TABLE OF CONTENTS ... 7
1 INTRODUCTION ... 9
1.1 Background and relevance of the topic ... 9
1.2 Research objectives and execution ... 11
1.3 Structure of the study ... 12
2 TECHNOLOGY ADOPTION AND ACCEPTANCE ... 14
2.1 Diffusion of innovations ... 15
2.1.1 Role of time in diffusion of innovations ... 17
2.1.2 Perceived attributes of innovations ... 18
2.2 Theory of reasoned action and theory of planned behaviour ... 19
2.3 Technology acceptance model ... 21
2.4 Extensions of technology acceptance model ... 23
2.4.1 External variables of perceived usefulness ... 24
2.4.2 External variables of perceived ease of use ... 25
2.4.3 Additional belief factors ... 27
2.4.4 Determinants of behavioural intention ... 27
2.5 Unified theory of technology acceptance and use of technology ... 28
2.6 Synthetises on technology acceptance research ... 30
2.7 Criticism towards technology acceptance research ... 32
2.8 Conclusions on technology acceptance research ... 33
3 MOBILE TECHNOLOGY ADOPTION ... 35
3.1 General outlines of mobile adoption research ... 36
3.2 Salient factors related to mobile technology adoption ... 37
3.2.1 Perceived usefulness and performance expectancy ... 37
3.2.2 Perceived ease of use and effort expectancy ... 38
3.2.3 Personal innovativeness ... 40
3.2.4 Social influence and subjective norm ... 43
3.2.5 Risk perception ... 44
3.2.6 Perceived costs ... 46
3.2.8 Perceived mobility, reachability and ubiquity ... 48
3.2.9 Perceived job relevance ... 50
3.3 Conclusion on mobile adoption research ... 51
4 SALES AUTOMATION TECHNOLOGY ... 54
4.1.1 Customer relationship management ... 55
4.1.2 Dimensions of mobility ... 57
5 METHODOLOGY ... 59
5.1 Research process: deductive and inductive reasoning ... 61
5.2 Conceptual research model ... 61
5.3 Hypothesis generation ... 62
5.4 Quantitative research approach ... 67
5.4.1 Online survey ... 68
5.4.2 General execution ... 68
5.4.3 Survey development ... 68
5.5 Statistical analysis technique: structural equation modelling (SEM) . 71 5.5.1 Covariance and variance based models ... 71
5.5.2 SEM analysis technique ... 73
5.5.3 Evaluation of outer model ... 74
5.5.4 Evaluation of inner model ... 76
6 RESULTS ... 79
6.1 Descriptive results ... 79
6.1.1 Collected data and survey respondents ... 79
6.1.2 Descriptive statistics for variable indicators ... 80
6.2 Confirmatory phase ... 86
6.3 Results of inner model ... 88
6.4 Findings ... 89
7 DISCUSSION ... 93
8 SUMMARY ... 96
REFERENCES ... 98
APPENDIX 1 REVIEWED LITERATURE ... 109
APPENDIX 2 DIMAR SURVEY VISUALISATION ... 116
APPENDIX 3 TEST OF NORMALITY ... 117
APPENDIX 4 INITIAL VALIDITY OF THE MEASUREMENT MODEL ... 118
APPENDIX 5 CROSS-CORRELATIONS ... 119
1 INTRODUCTION
Through the ages, organisations have sought new opportunities that would support their business activities and improve their position on the markets.
Technology has undoubtedly been one of the most significant contributors re- garding these goals. The ever increasing development of technology leads or- ganisations to constantly evaluate, whether new technology could advance their business activities. Similarly, the recent development of mobile technology has made organisations to consider the possibilities of mobile devices from per- spectives in which their use was not feasible in the past. This aspect underlines mobile adoption as an important and novel area of research.
On this basis, the main interest of this study is to examine salespeople’s in- tention to adopt mobile CRM (customer relationship management) solutions in industrial context. Sales work can be conceived to consist of activities, which are ideal targets to be supported by mobile technology. However, there is yet little information on what drives salespeople to adopt mobile technology to support their sales activities. We aim to contribute to this gap of knowledge in this study.
1.1 Background and relevance of the topic
Though the possibilities of mobile technology in organisational use are widely recognised, there is a lack of understanding on how to enhance business pro- cesses from the perspective of mobile technology and applications (Liang, Huang, Yeh, & Lin, 2007). Without a question mobile devices have revolution- ised the way how companies can enhance their internal and external communi- cation efforts. Though there have been efforts to apply mobile technology solu- tions to other activities as well, they remain to show their real edge.
An organisation may have an idea about adopting an innovation, but its everyday usage and actual benefits depend on how employees implement it (Talukder, 2012). Given these circumstances, two types of organisational adop-
tion decisions can be identified: 1) the decision made by an organisation, and 2) the decision made by an individual within an organisation. Although an organ- isation may have ability to influence employees' attitudes towards adoption, some employees are more compliant to adopt certain innovations than others.
(Frambach & Schillewaert, 2002.)
A deep understanding of adopter insight is essential in order for an organ- isation to implement an innovation successfully (Talukder, 2012). Organisa- tion’s consideration of whether to utilise the most recent mobile technology, or not, is however becoming irrelevant as employees are bringing their own mo- bile devices to the work environment nonetheless (Mansfield-Devine, 2012).
Related studies (e.g. Pedersen, 2005) have shown that mobile services adopted for personal use are often quickly adopted for work contexts. Another issue is whether mobile services differ from traditional information and communication technology services in ways that affect their adoption (Pedersen, 2005).
Mobile technology is associated with mobility and portability within general discussion (Liang et al., 2007). Mobility refers to the concept of physical move- ment within geographical space (Green, 2002), whereas portability comprises an idea that the given technology’s physical aspects support mobility (Junglas &
Watson, 2003). By adapting a perception by Perry, O'hara, Sellen, Brown, and Harper (2001), it can be concluded that saying “anytime, anywhere” can firmly be attached to the context of mobile technology. Mobile technology enables ubiquitous access to services through wireless networks and various devices (Liang et al., 2007), and staying socially connected on the move (López-Nicolás, Molina-Castillo, & Bouwman, 2008).
Wireless access to the Internet, in general, provides the means to perform any activity over the Internet with mobile devices (Wiratmadja, Govindaraju, &
Athari, 2012). As a consequence, business professionals can improve their productivity and consumers have more time for other tasks (Parveen & Sulaim- an, 2008). Mobile Internet has brought new opportunities, and overall reshaped the traditional telecommunication industry (Lu & Zhu, 2011). York and Pen- dharkar (2004) have argued that slow connection speeds, lack of processing power and the fast development of mobile devices have hindered the adoption of mobile technology for business context. However, the advent of fast mobile telecommunication network, increased processing power of mobile devices, and availability of mobile services, have extended the possibilities that mobile tech- nology has to offer for consumers and business users alike.
Mobile commerce encompasses all direct and indirect transactions via mo- bile devices (Liang et al., 2007). More precisely, mobile commerce refers to the use of a mobile device and network to “conduct transactions that result in the transfer of value in exchange for information, services or goods (Sadi & Noor- din, 2012, p. 40)”. Mobile commerce can be implemented in customer and sup- plier interfaces, but it can also be utilised within and across organisational boundaries (Sheng, Nah, & Siau, 2005). Mobile commerce applications give more freedom to perform commerce related tasks (Liang et al., 2007).
The utilisation of information systems’ potential has enhanced as the re- cent development of mobile technology has extended information access and collaboration beyond the traditional operational boundaries (Perry et al., 2001;
Er & Kay, 2005). As a consequence, the variety of work contexts is growing (Vartiainen & Hyrkkänen, 2010). This has served as a foundation to the essence of mobile workers who perform their business activities without regarding a fixed place. Their work is characterised by flexible use of time and place (Vartiainen
& Hyrkkänen, 2010). From mobile workers’ perspective, mobility plays a more vital role than for those who perform their tasks in the premises of the work- place (Kim & Garrison, 2009). As York and Pendharkar (2004) state, this aspect certainly poses requirements for tools that are used in mobile work situations.
Among others, salespeople are a group of professionals that are increas- ingly taking advantage of advanced mobile technology. Sales force automation (SFA) refers to "the use of computer hardware, software, and telecommunica- tions devices by salespeople in their selling and/or administrative activities (Morgan & Inks, 2001, p. 463)". In turn, customer relationship management (CRM) is an idea of building and maintaining customer relationships through sales, marketing, and customer service activities (Sinisalo, Salo, Karjaluoto, &
Leppäniemi, 2006). SFA embodies the concept of CRM as for technology. These concepts are further examined in the following chapters.
From academic perspective, this study contributes to the call of research of Marketing Science Institute (2012), which among other topics, prioritises re- search related to the impact of mobile platforms on markets. This prioritisation is targeted for the timeframe from 2012 to 2014. From practical perspective, the results of this study benefit service providers and organisations alike. As mobile technology and services are still an emerging trend, there is a growing demand to know more about the key antecedents of mobile adoption. Service providers undoubtedly welcome an idea of the attributes that are sought from mobile CRM systems. The same attributes benefit also organisations whose aim is to hasten the adoption of business favourable technologies through selecting solu- tions that meet employees’ expectations. As Buehrer, Senecal, and Pullins (2005) state, it is essential to understand the reasons behind salespeople's use of tech- nology, possible barriers of the use, and the means for assisting salespeople in the utilisation process.
1.2 Research objectives and execution
This study aims to shed light to mobile CRM adoption within industrial set- tings. The specific purpose of this research is to examine factors that are present in salespeople's adoption of mobile CRM solutions. We approach the adoption of mobile CRM from the perspective of technology acceptance by extending technology concentrated approaches (e.g., technology acceptance model, TAM) with findings from mobile adoption research. Research aims to uncover factors
that affect salespeople's adoption of mobile CRM in industrial settings, and an- swer to the following research questions:
1. What factors explain salespeople’s intention to adopt mobile CRM solutions?
2. What general factors can be identified in technology adoption and acceptance research?
3. Has mobile adoption research brought novel dimensions to general technology adoption research?
Modern laptops can compete with desktops as to functionalities and perfor- mance. Although they are used in mobile contexts, their usage does not exten- sively represent neither all possibilities nor limitations of mobility, nor salient characteristics of mobile devices. Moreover, laptops are seen to be a closer rep- resentation of desktops than that of modern mobile devices (as of spring 2012).
The technological interests of this study are therefore smartphones and tablets that can bring novel dimensions to the studies of CRM adoption.
This study takes a positivistic research approach. Empirical part of this study is carried out with a quantitative survey method, and structural equation modelling is used for further the analysis of the results. Study was conducted as a part of DIMAR (Digital Marketing Communications in Industrial Marketing) project and under guidance of Jyväskylä School of Business and Economics. As an academic research project, DIMAR was carried out by four Finnish universi- ties. The focus of DIMAR was to investigate marketing communications of in- dustrial firms. Through the project, specific research subject in this particular research is sales personnel from five Finnish B2B organisations. Four of these organisations cover pulp and paper, power solution and renewable energy in- dustries, and one represents education service firms (working in industrial con- text).
1.3 Structure of the study
The study begins with a literature review. The review aims to give an overall picture of the research field, gradually arriving to the specific contexts of this study. Chapter 2 concerns technology adoption and acceptance generally by introducing and critically evaluating commonly accepted diffusion and adop- tion models. The chapter is due to give an overall understanding of the theoret- ical background of the research topic. Chapter 3 takes more context aware view on technology adoption; the chapter examines former studies on mobile tech- nology adoption research. Former studies serve as a basis for empirical valida- tion, and thus, the chapter foregrounds the general constructs of the studies.
Finally, chapter 4 concentrates more closely on sales work and its assisting technologies.
Methodology in chapter 5 lays the grounds for empirical part of the study by introducing positivistic research approach and quantitative research method.
In addition, the chapter contains conceptual research model and hypothesis generation. Structural equation modelling is presented as the means of analysis.
Chapter 6 illustrates the descriptive results of the survey as well as the confirm- atory phase of the structural equation model. Chapters 7 and 8 discuss and summarise the research findings.
2 TECHNOLOGY ADOPTION AND ACCEPTANCE
The value propositions of information technology for businesses have empha- sised performance gains and competitive advantage. However, when compar- ing the available technology and its effect on performance, it can be stated that results are somewhat contradictory to the expectations (Agarwal & Prasad, 1997). Moreover, this dilemma is seen to emerge from end user’s non- acceptance of given technology. It can be discussed that even though the deci- sion to adopt technology has been made by the organisations management, the true performance gains from the technology can only be achieved when the end users adopts the technology a part of their work tasks (Agarwal, 2000). As a result to this problem, academia has tried to seek answers to what are the ante- cedents of successful technology acceptance. It can be even said that technology acceptance and adoption research are one of the most mature research areas in the information systems literature (Venkatesh, Morris, Davis, & Davis, 2003).
Technology adoption research is not only defined as multi-disciplinary re- search area with heavy influences from sociology and psychology (Venkatesh et al., 2003), but also by its multitude of approaches to adoption. To further elabo- rate: (1) research subjects can be organisations as a whole or individual users (Frambach & Schillewaert, 2002), (2) the different stages of adoption can be scrutinised, such as having pre-adoption and post-adoption (continued use) approach (Karahanna, Straub, & Chervany, 1999), and (3) how system design initiatives (such as interface design) can be altered in order to improve system acceptance (Gould, Boies, & Lewis, 1991). More general categorisation of tech- nology adoption research is made by Pedersen and Ling (2003). They distin- guish different research directions based on the stage of adoption and the level of analysis (individual and aggregate). Namely, these research directions are diffusion of innovations, adoption research, uses and gratifications research and domestication research. From these research directions, diffusion of innovations is seen to take a rather aggregate approach to adoption. This occurs for example through categorising different groups of people who have adopted an innova- tion, and taking into account communications network which mediates the ef- fects of the innovation. Adoption research on the other hand takes into account
the individual level of adoption and aims to explain why a specific technology is being adopted. This is achieved by employing explanatory variables such as utilitarian motivations, i.e. usefulness of the technology as a determinant of adoption. Uses and gratification research is very similar to traditional adoption research, but the selection of explanatory variables is made based on initial qualitative phase, which reveal the gratifications that users seek from the tech- nology. Finally, domestication research focuses on the consequences of the technology adoption, rather than focusing on the prediction of adoption. In domestication research, reference disciplines include sociology, anthropology and ethnology. Therefore, the focus of domestication research is on how tech- nology affects different contexts and relationships.
It can be stated that current technology adoption research is dominated by formerly described adoption research approach, with focus on individual's per- ception about the technology, and the individual's intention to use technology (Agarwal, 2000). Technology adoption research, by large, draws its conceptuali- sations from diffusion of innovations introduced by Rogers in 1962 (Davis, 1989;
Moore & Benbasat, 1991). Special focus is placed on the perceptions that an in- dividual has about an innovation (e.g. relative advantage over other innova- tions), which are seen to affect the adoption decision. Considering technology adoption research further, equal importance can be placed on the theory of rea- soned action (TRA) introduced by Fishbein and Ajzen (1975). TRA posits that individual’s beliefs about the consequences of behaviour are antecedent to in- dividual’s intention to behave. As a reference theory, TRA significantly contrib- utes to technology adoption research, as its belief constructs are often modified and utilised for the purposes of technology adoption research.
Simply put, diffusion of innovations provides the basis for perceptions about the technology in question and TRA provides theoretical robustness to model individual’s behaviour based on aforementioned perceptions.
This chapter introduces the development of technology adoption research from its early inception to more contemporary applications, furthermore, the aim is to form a baseline for subsequent mobile focused technology adoption literature review and its empirical application.
2.1 Diffusion of innovations
Innovation diffusion research has its roots in many separate fields. Research started during the 1940s and 1950s in independent sciences and areas of innova- tions, which later resulted in uncovering remarkably similar findings despite different research areas (Rogers, 2003, p. 39). Accordingly, research has revealed that many innovations diffuse in similar patterns (Brancheau & Wetherbe, 1990) which in part grounded Rogers’ theory of innovation diffusion to be applicable in multiple disciplines.
Diffusion of innovations takes into account multiple variables that are seen to affect the rate of adoption (figure 1) and as such, is seen to take a rather
aggregate view on the spread of innovations (Straub, 2009). Among perceived attributes of innovations, aspects such as type of innovation decision (optional, collective, or authoritarian), communication channels, nature of social systems and promotion efforts are seen as variables which affect the rate of adoption.
Considering all these attributes, innovations diffusion theory covers a wide range of issues, from aggregate to more individual level. Given these considera- tions and the focus of this work, issues applicable to individual level are con- sidered more thoroughly in this study.
FIGURE 1 Variables determining the rate of adoption of innovations (Rogers, 2003, p. 222)
Innovations are the focal point in Rogers's diffusion of innovations theory. In- novations are new means for solving problems and exploiting opportunities (Brancheau & Wetherbe, 1990). Specifically, "an innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption (Rogers, 2003, p. 12)". In turn Rogers (2003, p. 5) defines diffusion as "the pro- cess in which an innovation is communicated through certain channels over time among the members of a social system". Thus the four main elements of diffusion can be specified as 1) innovation, 2) communication channels, 3) time, and 4) the social system (figure 2) (Rogers, 2003, p. 11).
I. Perceived attributes of innovations Variables determining the
rate of adoption
Dependent variable that is explained
II. Type of innovation-decision 1. Relative advantage 2. Compatibility 3. Complexity 4. Trialability 5. Observability
1. Optional 2. Collectibe 3. Authority
III. Communication channels (e.g., mass media or interpersonal)
Rate of adoption of innovations
IV. Nature of social system (e.g., its norms, degree of network interconnectedness, etc.)
V. Extent of change agents’ promotion efforts
FIGURE 2 The diffusion process (Rogers, 2003, p. 11)
2.1.1 Role of time in diffusion of innovations
Time is especially important part of diffusion of innovations theory. Rogers states that time is involved in (1) individual’s decision process to adopt or reject an innovation, (2) definition of relative innovativeness of a unit of adoption and, (3) S-shaped (as one of many possible shapes) rate of adoption (as shown in fig- ure 2).
It is important to stress that innovation adoption does not happen in one point of time, but is seen as a process. Firstly, a decision making unit e.g. indi- vidual has to have knowledge about the existence of an innovation (for example mass media as a change agent). The next phase of innovation adoption is de- fined as persuasion, in which favourability towards the innovation is formed (affected by perceived characteristics of innovation, which will be discussed later). Decision to adopt follows when individual performs activities leading to rejection or adoption of the innovation. More thorough adoption occurs, when individual puts the innovation into use in the actual implementation phase. Fi- nally, confirmation takes place when an individual seeks to reinforce the adop- tion decision, however, conflicting messages about the innovation can still cause the reversion of previous decision. (Rogers, 2003, p. 20.) Technology adoption theories usually presume that knowledge about the technology has already been acquired, so the emphasis is especially on the decision phase and other subsequent phases of the decision making process.
Adopter categorisation in innovation diffusion theory is based on the "innova- tiveness" of the members of a social system. Innovativeness can be defined as
“the degree to which an individual or other unit of adoption is relatively earlier in adopting new ideas than other members of a social system (Rogers, 2003, p.
280)”. Based on this criterion, Rogers (2003, p. 280) has classified the base of adopters into five groups: innovators, early adopters, early majority, late major-
0%
10%
20%
30%
40%
50%
60%
70%
Time
Percent of adoption
80%
90%
100%
Take-off
Earlier adopters
Innovation I Innovation II Innovation III
ity, and laggards (figure 3). The meaningfulness of these categories arise in comparison of socio-economic characteristics of different adopters. To make harsh generalisations, earlier adopters have received more formal education, have higher social status, and have greater knowledge of innovations than later adopters (Rogers, 2003, p. 288−291).
FIGURE 3 Adopter categorisation on the basis of innovativeness (Rogers, 2003, p. 281)
As discussed, time of adoption indicates the relative innovativeness of an indi- vidual. However, this approach has also attracted criticism. For example Flynn and Goldsmith (1993) argue that innovativeness is an abstract construct without means to assess its reliability and validity. Agarwal and Prasad (1998) also state, that measuring innovativeness gives little practical information, as it is post- adoption indicator. To overcome this problem, Agarwal and Prasad (1998) have developed psychometric means to measure personal innovativeness.
2.1.2 Perceived attributes of innovations
In short, it can be stated that diffusion of innovations describes how potential users make decisions about rejecting or adopting an innovation based on their beliefs towards the innovation (Moore and Benbasat, 1991). To contribute this idea, Rogers (2003, pp. 15−16) proposes that innovations have five perceived attributes which act as key indicators when an individual decides to adopt an innovation:
1. Relative advantage is the degree to which an innovation is perceived to be better than the one it supersedes.
2. Compatibility is a construct which includes how an innovation is com- patible with the prior experience, values and needs of an individual.
3. Complexity refers to the degree to how easy or difficult it is to under- stand an innovation or to use it.
4. Trialability describes the degree to which an innovation can be tested before its adoption.
2,5% 13,5% 34% 34% 16%
Early majority
Late
majority Laggards Early
adopters Innovators
5. Observability is a concept of how well the results of an innovation are visible to others. In other words, the easier it is to perceive the benefits that an innovation has on other people, the more likely an individual is to adopt the innovation in question.
A notable utilization of Rogers’ (1962) innovation diffusion theory within the context of information technology is that of Moore and Benbasat (1991). They developed a scales to measure perceptions about using an information technol- ogy innovation. Moore and Benbasat (1991) found that observability, originally defined by Rogers taps into two separate constructs: (1) result demonstrability, which shows tangible results of using an innovation and (2) visibility, which posits how visible potential adopters perceive the benefits of an innovation to be. Moore and Benbasat (1991) also included image construct to be a separate variable from relative advantage introduced by Rogers. In short, image con- struct is regards how the innovation in question will improve individual’s so- cial status. Finally, they added voluntariness of use, which is the concept of how compulsory the innovation adoption is perceived to be. Innovation adop- tion can e.g. be a mandate from superiors in organizational settings.
However, there is a lack of research in testing such a comprehensive set of beliefs about perceptions of innovations as suggested by Moore and Benbasat (1991). Agarwal and Prasad (1997) suggest that a more parsimonious set of be- liefs could be more valid predictor of innovation acceptance: moreover they suggest in their research about characteristics of World Wide Web as an emerg- ing technology, that result demonstrability, relative advantage, compatibility, visibility, trialability and voluntariness emerged as significant predictors of user acceptance. Further, an early meta-analysis performed by Tornatzky and Klein (1982) also point out that relative advantage and complexity, along with com- patibility are the factors that affect the innovation adoption the most.
When considering individuals as adopters in technology adoption re- search in terms of innovation diffusion theory, it can be concluded that the per- ceived characteristics of innovation are a recurring concept (e.g., Karahanna et.
al., 1999; Plouffe, Hulland, & Vandenbosch, 2001) and as such attract more at- tention than other conceptualisations of innovation diffusion theory (Jeyaraj, Rottman, & Lacity, 2006).
2.2 Theory of reasoned action and theory of planned behaviour
Fishbein and Ajzen (1975) introduced theory of reasoned action (TRA), from which models such as Davis’s (1989) technology acceptance model (TAM) draws its theoretical background. TRA (figure 4) posits that individual’s beliefs about outcomes of certain behaviour (e.g. computers help students to perform better), together with evaluations of these outcomes, such as level of desirability (the importance of performing better) form an attitude. Attitude together with social norm are seen as determinants of behavioural intention. Social norm con-
sists individual's evaluations of opinions that for example his/her co-workers have about certain behaviour. These evaluations are again reflected against willingness to comply with the opinions of others. (Fishbein & Ajzen, 1975, p.
16). The underlying rationale in measuring intention as a key variable lies in its strong correlation to actual future behaviour. (Fishbein & Ajzen, 1975, p. 381—
382.) Furthermore, Ajzen (1991) elaborates that:
Intentions are assumed to capture the motivational factors that influence a behaviour;
they are indications of how hard people are willing to try, of how much of an effort they are planning to exert, in order to perform the behavior (Ajzen, 1991, p. 181).
Innovation diffusion theory is similar to TRA in respect to an individual’s deci- sion making process: an individual evaluates innovation characteristics and based on those evaluations, forms an attitude towards the innovation which affects the usage decision. However, TRA aims to conceptualize the formation of attitude as an antecedent to intention, and thus provide more explanatory power to the technology adoption research. (Karahanna et al., 1999.) Dillon and Morris (1996) point out that although TRA is very general model, and applica- ble to several contexts, it is a baseline model for many technology specific mod- els, and thus, it is important to understand its constructs.
FIGURE 4 Theory of reasoned action (Davis, Bagozzi, & Warshaw, 1989, p. 984)
TRA does not come without limitations. For example it does not consider situa- tions where individuals do not have volitional control over their behaviour.
Ajzen (1991) has addressed this by adding perceived behavioural control to TRA, thus forming theory of planned behaviour (TPB) (figure 5). Behavioural control takes into account controlling factors that are independent of the actor (such as monetary resources i.e. actual behavioural control). However, more interesting to researchers are individual’s internal control factors such as self- confidence in performing a behaviour in question. For example, if two individ- uals with similar external resources are considering to go hill climbing, the one with more confidence in doing so, will most likely engage with the behaviour.
Behavioural control has its roots in Bandura’s (1982) conceptualisation of self-efficacy. Bandura (1986) stresses that self-efficacy is not concerned with what actual skills an individual has, but rather, about judgements of individu- als capabilities to perform with the skills that she/he has.
As in TRA, intention to behave is a central construct also in TPB. Howev- er, according to Ajzen (1991) in order for intention to find actualisation, the be- haviour has to be under volitional control of an individual.
Intention to perform
behaviour Behaviour
Attitude toward behaviour Beliefs
and evaluations
Normative beliefs and motivation
to comply
Subjective norm concerning behaviour
Even though TPB has been successfully applied to explain behaviour in multiple areas of interests (Ajzen, 1991), the utilization of TPB within the con- text of information technology has not yielded conclusive results compared to more parsimonious models in the field of technology acceptance research (Mathieson, 1991). Mathieson found that while TPB was able to be more ex- planatory, technology acceptance model (discussed later) performed better in terms of variance explained. Similar conclusions were later made by Chau and Hu (2002) by confirming the effect of perceived usefulness (TAM based con- struct) to be more significant on behavioural intention than the constructs of TPB.
FIGURE 5 Theory of planned behaviours (Ajzen, 1991) (adapted from Mathieson, 1991, p.
175)
2.3 Technology acceptance model
Technology acceptance model (figure 6) introduced by Davis in 1989 is the most widely used theoretical model when attempting to explain technology adoption (Lee, Kozar, & Larsen, 2003). This is not only due to its empirical validation (Mathieson, 1991; Taylor & Todd, 1995b; Agarwal & Prasad, 1999) but also due to its parsimonious utilization compared to competing models (Mathieson, 1991). By drawing theoretical background from the theory of reasoned action, TAM consists of two belief factors which are perceived usefulness and per- ceived ease of use. According to Davis (1989) perceived usefulness is “the de- gree to which a person believes that using a particular system would enhance his or her job performance (p. 320)", whereas perceived ease of use refers to "the degree to which a person believes that using a particular system would be free of effort (p. 320)”. These constructs are very similar to two perceptions present- ed in Roger’s diffusion of innovations. Namely perceived usefulness is seen to reflect the construct of relative advantage, and perceived ease of use is seen to be very similar to complexity, though being an opposite construct.
Intention to perform
behaviour Behaviour
Attitude toward behaviour
Actual behavioural control e.g. monetary
resources
Beliefs and evaluations
Normative beliefs and motivation
to comply Control beliefs
and perceived faciliation
Perceived behavioural
control Subjective norm concerning behaviour
FIGURE 6 Technology acceptance model (Davis et al., 1989, p. 985)
Attitude, i.e. individual's positive or negative feelings (evaluations) towards a certain object (Fishbein and Ajzen, 1975, p. 216), is seen to mediate the effect of perceived ease of use and perceived usefulness towards behavioural intention (Davis, 1989). TAM also consists an idea that perceived ease of use does not on- ly affect behavioural intention by mediating through attitude, but works through perceived usefulness. The rationale behind this relationship is that easy to use technology is also perceived to be useful. Perceived usefulness has also been found to have a direct effect on behavioural intention above attitude. This relationship can be rationalised in the following way: in work contexts technol- ogy can be used regardless of negative attitude towards it, as the technology in question is perceived to be means to achieve an outcome. (Taylor & Todd, 1995b.) This relationships has also contributed to early modifications of TAM:
As attitude has an inconsistent mediating role between beliefs and intention, it has been omitted from the latter model (Davis et al., 1989). The rationalisations for this inconsistency has been sought from the idea, that regardless whether an individual holds a negative attitude towards a system, the individual will con- tinue to use the system, as he or she perceives the system use to be imperative to their success in work. Given these considerations, attitude has a somewhat equivocal role in technology adoption research. For example Legris, Ingham and Collerette (2003) found that the relationship between attitude and behav- ioural intention had a positive relationship in seven out of eleven studies, whereas the relationship was not considered in 17 out of 22 studies. Even though attitude is often omitted from TAM studies, its inclusion can be rational- ised e.g. by the means of how much control individuals exert over their work.
To further elaborate, salespeople can be relatively emancipated in respect to what information technology tools to use or not to use, thus being able to act based on their attitudes (Robinson, Marshall, & Stamps, 2005).
TAM core beliefs stem from the idea that individuals evaluate their moti- vations to perform a certain behaviour (perceived usefulness) and how much effort it requires to perform that behaviour (perceive ease of use) (Davis, Bagoz- zi, & Warshaw, 1992). Originally TAM was developed for organisational and work context (Davis, 1989; Davis et al., 1989). This is especially explicit in terms of how human motivation is regarded being strictly utilitarian in TAM. Contra- ry to this, the theory related to human motivation has generally distinguished intrinsic and extrinsic motivations. Intrinsic motivations emphasize the concept that tasks are performed due to their enjoyable or inherently interesting nature, whereas extrinsic motivations are performed with an expectation of certain goal
Perceived usefulness External
variables Behavioral
intention Attitude
toward using Perceived
ease of use
or outcome. (Davis et al., 1992.) Extrinsic motivations are especially explicit in work context e.g. in their work salespeople are expected to increase their sales.
It can be discussed that due to the fact, how perceived usefulness measures the instrumental role of technology as means for individuals to achieve set goals, it has also been a pervasive denominator of technology adop- tion across several studies (Taylor & Todd, 1995a; Jiang, Hsu, Klein, & Lin, 2000;
Horton, Buck, Waterson, & Clegg, 2001). Perceived usefulness also holds its ground when examining use antecedents of experienced and inexperienced us- ers, whereas the effect of perceived ease of use diminishes for experienced users (Venkatesh & Davis, 2000; Taylor & Todd, 1995a). Also in general, perceived ease of use has found to be less significant denominator of behavioural inten- tion than perceived usefulness (Davis, 1993) or even non-significant (Hu, Chau, Sheng, & Tam, 1999). Gefen and Straub (2000) offer an explanation for the vary- ing effects of perceived ease of use on behavioural intention. Gefen and Straub see that perceived ease of use captures the previously discussed intrinsic moti- vation by having a more prominent role in tasks that involve only technology use situations. For example perceived ease of use is due to have a more promi- nent effect when an individual searches for items in an e-commerce site. On the contrary perceived usefulness captures the act of ordering the items. Gefen and Straub conclude that the effects of perceived ease of use depend on the context under investigation.
2.4 Extensions of technology acceptance model
Though TAM is a robust theory in explaining behaviour with parsimonious and reliable instruments, there are still aspects which it is not seen to cover. Re- searchers have for example criticised TAM for not modelling non-volitional adoption situations (Mathieson, Peacock, & Chin, 2001) or that TAM does not consider social influences in a sufficient manner (Venkatesh & Davis, 2000).
TAM has been extended to include these concepts, along with many other.
The relationships of these concepts to the original TAM are manifold and at times lack consistency. For example Legris et al. (2003) point out that external variables are applied to TAM inconsistently across different studies.
Wixom and Todd (2005) attempt to illustrate the most popular extensions to TAM (figure 7). Their illustration also serves as a good baseline for introduc- tion how TAM has been extended in general. The extensions will be dealt in order as per figure 7, from left to right, starting with how core beliefs of TAM have been extended, gradually arriving to extensions determining intention to use.
FIGURE 7 Popular extensions to TAM (adapted from Wixom & Todd, 2005)
2.4.1 External variables of perceived usefulness
Venkatesh and Davis (2000) identified general antecedents of perceived useful- ness and introduced technology acceptance model 2 (TAM2) (figure 8). Accord- ing to their study perceived usefulness is determined by social constructs, namely subjective norm and image, along with cognitive instrumental process- es (system specific characteristics) i.e. job relevance, output quality, result de- monstrability, and perceived ease of use. Voluntariness of use and past experi- ence are regarded as moderators.
FIGURE 8 Technology acceptance model 2 (adapted from Venkatesh & Davis, 2000, p. 188)
Related to social construct, Venkatesh and Davis introduce three social influ- ence mechanism, specifically compliance, identification and internalisation (originally introduced by Kelman in 1958). Compliance represents a situation where an individual behaves in order to avoid punishment or to attain a certain goal. This situation is modelled with the direct relationship of social influences towards behavioural intention. Accordingly, this effect is moderated by volun- tariness, but also by individuals past experience with the system. Internalisation on the other hand captures the situation where individual incorporates other's beliefs structures into individual's own belief structure. This situation is mod- elled as a direct effect of social influence towards perceived usefulness. Again,
Perceived ease of use
Attitude toward usage
Intention to
use Usage
Perceived usefulness External variables
(e.g. demographics, system characteristics, personality traits)
Additional belief factors (e.g. trialability, compatibility)
Factors from related models (e.g. subjective norm, perceived behavioral control)
Perceived usefulness Subjective
norm
Experience Voluntariness
Image Job relevance Output quality Result
demonstrability
Usage behaviour Intention
to Perceived Use
ease of use
the relationship is moderated by experience. When considering the relation- ships between social influences and both, behavioural intention and perceived usefulness, a moderating effect of experience can be found: as individual gains more experience with the system, the effect of social influences is seen to dimin- ish. Finally, identification is a concept where behaviour is seen to increase one's social status (image), as the behaviour is seen favourably by others. According to Venkatesh and Davis (2000), identification is modelled via social influences affecting image, and image affecting perceived usefulness.
Cognitive instrumental processes capture how individuals compare the capabilities of the system and what they perceive to be crucial to succeed in their tasks. Essentially, these issues are reflected by job relevance, output quali- ty, and result demonstrability. These constructs are seen to determine perceived usefulness directly (Venkatesh & Davis, 2000).
2.4.2 External variables of perceived ease of use
Venkatesh (2000) suggests that an individual forms his or hers perceptions about the ease of use of a specific system according to general information and beliefs about technology use (figure 9). These pieces of information and beliefs act as anchors. The anchors are conceptualised as (1) internal control (individu- al’s self-confidence to perform a behaviour), (2) external control (e.g. organisa- tional support factors), (3) intrinsic motivation (computer playfulness) and (4) emotion (computer anxiety). As individual gains more experience with the sys- tem the effect of these anchors is seen to change. Moreover adjustments, which are objective usability and perceived enjoyment, begin to determine perceived ease of use as user gains more experience. Gradually computer playfulness and computer anxiety are seen to diminish over time but external and internal con- trol factors maintain their effect on perceived ease of use. (Venkatesh, 2000.)
FIGURE 9 Determinants of perceived ease of use (Venkatesh, 2000, p. 346) Computer
self-efficacy Facilitating conditions Computer anxiety Computer playfulness
Perceived enjoyment Adjustments Anchors
Objective usability
Perceived usefulness
Usage behaviour Intention
to Perceived Use
ease of use
By incorporating antecedents of perceived usefulness (Venkatesh & Davis, 2000) and antecedents of perceived ease of use (Venkatesh, 2000), Venkatesh and Bala (2008) have proposed technology acceptance model 3 (figure 10). To put simply TAM3 considers antecedents of both, perceived ease of use and per- ceived usefulness and also add the moderating effect of experience to additional relationships.
Venkatesh and Bala (2008) argue, that the determinants of perceived ease of use and perceived usefulness do not have cross-over effects. By this they mean, that for example an individual does not form opinions about systems ease of use through social influences, but rather through his/hers own experi- ences and previously discussed anchors. This is however contradictory to Ban- dura’s idea of social learning (1986), where individuals do not only learn through their experience, but by observing others, and thus influence individu- als’ perceptions of self-efficacy, and further, perceptions of ease of use. Given these considerations, and actual lack of empirical evidence about cross-over effects of TAM3, it can be stated that the relationships of TAM3 are yet not ma- ture and throughoutly investigated.
FIGURE 10 Technology acceptance model 3 (TAM3) (Venkatesh & Bala, 2008, p. 280) Computer
self-efficacy Facilitating conditions Computer anxiety Computer playfulness
Perceived enjoyment Adjustments Anchors
Objective usability
Perceived usefulness
Usage behaviour Intention
to Perceived Use
ease of use Subjective
norm
Experience Voluntariness
Image Job relevance Output quality Result
demonstrability
2.4.3 Additional belief factors
The traditional rationalisation for inclusion of more belief factors to TAM follow the diffusion of innovations research (Moore & Benbasat, 1991; Rogers, 2003) which states that not only benefit (perceived usefulness or relative advantage) and effort (perceived ease of use or complexity) determine technology use (Karahanna et al., 1999; Plouffe et al., 2001). As a class example Karahanna et al.
(1999) demonstrate that richer set of beliefs explain more pre-adoption deci- sions. They incorporated perceived characteristics of innovations (PCI), namely trialability, result demonstrability, visibility and image along with perceived usefulness and perceived ease of use as antecedents of attitude. As mentioned, in pre-adoption situations users regard wider set of beliefs when deciding to use innovations. However, in continued use situations, only image and per- ceived usefulness remained significant determinants of attitude. It is very logi- cal that concepts such as trialability, visibility and result demonstrability lose their effect when individual continues to use innovation or technology. Plouffe et al. (2001) found that PCI explained more variance on intention to use than TAM. They further state that while TAM is parsimonious, the usage of PCI can provide more to practical information.
2.4.4 Determinants of behavioural intention
As TAM assumes that an individual can always decide whether to use the tech- nology in question or not, it can be stated that its ability to explain behaviour under volitional conditions is questionable (Mathieson et al., 2001). To address this Mathieson et al. (2001) propose that individual's behaviour can be affected by resources which can either facilitate or prevent behaviour to find actualisa- tion. They tested a very similar construct (i.e. perceived resources) as that of perceived behavioural control in TPB to be included in TAM. They found sup- port for the inclusion of resources, but also state that in settings where individ- ual is not stressed by resources, TAM performs well.
Another example for direct determinant of behavioural intention can be sought from the emergence of e-commerce. E-commerce has awoken practitioners and academia to consider trust issues as an antecedent to behaviour, as e.g. the ab- sence of human contact in e-commerce may awake distrust (Gefen, Karahanna,
& Straub, 2003). Gefen et al. (2003) found empirical support for the relationship of trust on behavioural intention. They concluded that trust is a separate factor from technology related beliefs (ease of use and usefulness) and it is linked di- rectly to intention and perceived ease of use. The study of, both Mathieson et al.
(2001) and Gefen et al. (2003) serve as an example how TAM can be extended by incorporating additional factors affecting behavioural intention.
As an overall conclusion to the discussion related to TAM extensions, it should be stressed that the context of research often determines the relation- ships of included constructs. For example, social influences can affect behav- ioural intention under mandatory use (as demonstrated by Venkatesh and Da-
vis, 2000), but when the use is voluntary, the role of social influences to behav- ioural intention can be non-existent. This consideration can also be a reason for such a diverse extensions of TAM in the field of technology adoption research.
2.5 Unified theory of technology acceptance and use of technolo- gy
Venkatesh et al. (2003) reviewed eight dominant theories used in explaining technology acceptance and innovation adoption. As an outcome they developed Unified Theory of Acceptance and Use of Technology (UTAUT) (figure 11). One of the main rationales in forming UTAUT was that many constructs in existing theories were similar in nature. Thus, by defining and validating UTAUT Ven- katesh et al. believed that researchers would be freed of integrating and validat- ing their own models in the field of information systems research.
FIGURE 11 Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003, p.
447)
UTAUT core constructs consist of performance expectancy, effort expectancy, social influences and facilitating conditions. In these terms UTAUT can be seen as a combination of TAM and TPB: Performance and effort expectancy are be- ing derived from TAM, and facilitating conditions refer to the external re- sources of perceived behaviour control of TPB, and social influences as such is a part of TPB. However, UTAUT also introduces moderating variables which are age, gender, experience and voluntariness of use. Theoretically, experience and voluntariness of use are also considered e.g. in TAM2, but in addition Ven- katesh et al. (2003) argue that gender and age as social constructs moderate technology use, and further, age as a moderator can also be considered to affect through weakening of cognitive abilities.
In terms of explained variance UTAUT performs well compared to TAM and its subsequent extensions. Whereas TAM explains around 40 % of the vari- ance of behavioural intention to use technology, UTAUT consistently explains around 60 % of the variance in use (Venkatesh et al., 2003).
Experience Age
Gender Facilitating conditions Social influences Effort
expectancy Use
behavior Performance
expectancy
Behavioral intention
Voluntariness of use
Given these results, UTAUT seems to be the model of choice in terms of variance explained. Along with these considerations and given the unified na- ture of UTAUT, one would be lead to think that UTAUT would have gained foothold as a prominent model in explaining technology adoption since its in- troduction. However, a recent literature review conducted by Williams, Rana, and Dwivedi (2012) of studies citing UTAUT revealed that 16 out of 450 studies actually utilised UTAUT (without moderating effects), whereas most of the cita- tions merely referred to UTAUT in means of supporting an argument, or criti- cising it. Also contradictory to the proposition of UTAUT being a unified model, the literature review also revealed that considerable amount of studies extend- ed UTAUT (similarly to TAM) (Williams et al., 2012). Thus, Williams et al. con- clude that further work is needed when forming a unified theory on technology acceptance such as UTAUT.
As pointed out, technology acceptance research in general focuses on technology acceptance in organisational and work context. Hence, it follows that the studied factors (e.g., utilitarian motives and facilitating conditions) em- phasise this particular context. However, consumerisation as a phenomenon has led to a situation, where employees are increasingly bringing their own per- sonal devices to work context. This is a fruitful area for academia as well. For example, it would be of great interest to investigate whether factors influencing personal use affects the decisions to transform the particular technology for work use as well.
Venkatesh, Thong, and Xu (2012) extended UTAUT to account for con- sumer technology acceptance. They perceived that intrinsic motives i.e. enjoy- ment reflect the hedonic value which consumers may seek from technology use.
Also cost issues are considered, as consumer, opposed to organisational users, are constrained by actual costs of a given technology. Finally, Venkatesh et al.
(2012) consider habit as a factor affecting behaviour. The rationale is that, as user's engagement with a certain behaviour becomes automatic, it turns into a habit, and as a consequence to this, individual becomes unaware of other be- havioural options (Verkplanken & Aarts, 1999). Habit is also regarded as an important alternative to behavioural intention within IS literature (Venkatesh, Davis, & Morris, 2007).
Even though Venkatesh et al. (2012) stress the importance of consumer technology acceptance research as an independent research area, the im- portance of it can also been seen in the light of consumerisation studies. If a salesperson for example decides to bring his/her own tablet to work context, the decision to do so derives from the original use context i.e. the same individ- ual as a consumer.
